wehub-resource-sync
119 lines
3.8 KiB
C++
119 lines
3.8 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/shuffle_batch_kernel.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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namespace phi {
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template <typename T, typename Context>
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void ShuffleBatchKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& seed,
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int startup_seed,
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DenseTensor* out,
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DenseTensor* shuffleidx,
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DenseTensor* seed_out) {
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auto x_embed_size = x.dims()[x.dims().size() - 1];
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int elem_size = 1;
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for (auto i = 0; i < x.dims().size() - 1; i++)
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elem_size *= static_cast<int>(x.dims()[i]);
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std::vector<int64_t> idx_vec; // record shuffled order
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idx_vec.reserve(elem_size);
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for (int i = 0; i < elem_size; i++) {
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idx_vec.push_back(i);
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}
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int64_t seed_int = 0;
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if (seed.initialized()) {
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seed_int = *seed.data<int64_t>();
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} else {
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seed_int = startup_seed;
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}
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std::default_random_engine engine;
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engine.seed(seed_int);
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auto custom_random_shuffle = [&idx_vec]() {
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std::random_device rnd;
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int64_t seed_tmp = rnd();
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std::default_random_engine rng(seed_tmp);
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const int n = static_cast<int>(idx_vec.size());
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std::vector<int> v(n);
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std::iota(v.begin(), v.end(), 0);
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std::vector<bool> visit(n, false);
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while (!v.empty()) {
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std::shuffle(v.begin(), v.end(), rng);
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int tmp = v.back();
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v.pop_back();
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if (v.empty()) {
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std::uniform_int_distribution<int> distr(0, n - 2);
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idx_vec[tmp] = tmp;
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std::swap(idx_vec[tmp], idx_vec[(distr(rng) + tmp + 1) % n]);
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return;
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}
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visit[tmp] = true;
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std::shuffle(v.begin(), v.end(), rng);
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int curr = v.back();
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v.pop_back();
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v.push_back(tmp);
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idx_vec[tmp] = curr;
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while (!visit[curr]) {
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visit[curr] = true;
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std::shuffle(v.begin(), v.end(), rng);
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idx_vec[curr] = v.back();
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v.pop_back();
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curr = static_cast<int>(idx_vec[curr]);
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}
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}
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};
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custom_random_shuffle();
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// change shuffle to custom_random_shuffle
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// std::shuffle(idx_vec.begin(), idx_vec.end(), engine);
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// ShuffleIdx record shuffle order
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shuffleidx->Resize({(int64_t)idx_vec.size()});
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auto* shuffleidx_data = dev_ctx.template HostAlloc<int64_t>(shuffleidx);
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for (size_t i = 0; i < idx_vec.size(); i++) {
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shuffleidx_data[i] = idx_vec[i];
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}
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// copy data according to idx_vec
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auto* x_data = x.data<T>();
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auto* out_data = dev_ctx.template HostAlloc<T>(out);
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for (auto i = 0; i < elem_size; i++) {
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memcpy(out_data + idx_vec[i] * x_embed_size,
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x_data + i * x_embed_size,
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x_embed_size * sizeof(T));
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}
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// set new seed
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seed_out->Resize({1});
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auto* seed_out_data = dev_ctx.template HostAlloc<int64_t>(seed_out);
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*seed_out_data = engine();
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}
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} // namespace phi
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PD_REGISTER_KERNEL(shuffle_batch,
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CPU,
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ALL_LAYOUT,
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phi::ShuffleBatchKernel,
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float,
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double,
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int32_t,
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int64_t) {
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kernel->OutputAt(1).SetDataType(phi::DataType::INT64);
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kernel->OutputAt(2).SetDataType(phi::DataType::INT64);
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}
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